A comparison of the Allplex™ bacterial and viral assays to conventional methods for detection of gastroenteritis agents
Bibliographic record
Abstract
OBJECTIVE: Molecular methods to detect diarrheal pathogens are increasingly being used in place of conventional methods. We compared a new multiplex real-time PCR assay for detection of both bacterial and viral gastroenteritis agents, the Allplex™ Gastrointestinal Panel Assays (AGPA), to conventional methods (stool culture for bacterial pathogens and electron microscopy (EM) for viral pathogens). RESULTS: Gastrointestinal viruses, in particular norovirus genogroup II viruses, were detected by the AGPA in a high number of specimens that were negative by EM. For bacterial pathogens, the AGPA was able to detect the organisms grown in culture with high sensitivity and additionally detected several types of E. coli, such as enteropathogenic E. coli (EPEC), enteroaggregative E. coli (EAEC), and non-O157 Shiga toxin-producing E. coli (STEC), that could not be detected with conventional culture methods. Overall, the AGPA had a > 2-fold higher detection rate than the conventional methods, with 24/135 (17.8%) samples positive by conventional methods and 60/135 (44.4%) by AGPA. Thus, diarrhea pathogen detection rates increased substantially with the use of the AGPA as compared to conventional methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".